Does location matter? Investigating the spatial and socio-economic drivers of residential energy use in Dar es Salaam
Bibliographic record
Abstract
Abstract Africa is set to become a key contributor to global energy demand. Urban growth and the energy use of city residents will drive much of the region’s changing energy picture. However, few studies have assessed residential energy use among African cities, and the heterogeneity in energy use at the sub-city scale. We use the case of Dar es Salaam, which is among Africa’s fastest-growing cities, and to our knowledge, present the first disaggregated estimates of residential energy use at the ward level. We show three main findings. First, we find a statistically significant difference in mean residential energy use among the surveyed wards, which group into four clusters representing distinct levels of household and transport-related energy use. These results show that mean residential energy use (the sum of household and transport-related energy use) is not always correlated with the socio-economic or spatial characteristics of wards—e.g. Msasani (high-income, formal ward) showed similar residential energy use as Keko (low-income, informal ward). Second, we show differences in energy use and fuel switching that occur between low-income and high-income wards: wood fuel (i.e. charcoal) is a majority contributor to residential energy use in low-income wards (Buguruni, Keko and Manzese), compared to gas, electricity and transport oils in high-income wards (Msasani and Kawe). Finally, regression models indicate that ward density has a statistically significant effect on transport-related energy use, while fuel stacking and proxies for household wealth have a statistically significant effect on household-related energy use. To conclude, we recommend that policymakers account for ward level differences in residential energy use when crafting energy sector strategies for Dar es Salaam (e.g. electrification, energy-efficient cooking, or public transportation initiatives). Policymakers may also anticipate possible convergence towards higher levels of energy use and a shift towards modern fuels, as wards develop socio-economically over time.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".